Wikidata + Python Pandas.

wikidata+pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sparqlwrapper\n",
      "  Using cached SPARQLWrapper-1.8.0.zip\n",
      "Collecting rdflib>=4.0 (from sparqlwrapper)\n",
      "  Using cached rdflib-4.2.2-py3-none-any.whl\n",
      "Requirement already satisfied: pyparsing in /home/sirex/.venvs/py36/lib/python3.6/site-packages (from rdflib>=4.0->sparqlwrapper)\n",
      "Collecting isodate (from rdflib>=4.0->sparqlwrapper)\n",
      "  Using cached isodate-0.5.4.tar.gz\n",
      "Building wheels for collected packages: sparqlwrapper, isodate\n",
      "  Running setup.py bdist_wheel for sparqlwrapper ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/sirex/.cache/pip/wheels/3c/b6/36/90a0877c0a7ac2933a16c446b535af632831b3fd692e1910fe\n",
      "  Running setup.py bdist_wheel for isodate ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/sirex/.cache/pip/wheels/61/c0/d2/6b4a10c222ba9261ab9872a8f05d471652962284e8c677e5e7\n",
      "Successfully built sparqlwrapper isodate\n",
      "Installing collected packages: isodate, rdflib, sparqlwrapper\n",
      "Successfully installed isodate-0.5.4 rdflib-4.2.2 sparqlwrapper-1.8.0\n"
     ]
    }
   ],
   "source": [
    "!pip install sparqlwrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "\n",
    "from pandas.io.json import json_normalize\n",
    "from SPARQLWrapper import SPARQLWrapper, JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.style.use('ggplot')\n",
    "mpl.rc('font', family='Ubuntu', size=16)\n",
    "mpl.rc('figure', figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select(query, service='https://query.wikidata.org/sparql'):\n",
    "    sparql = SPARQLWrapper(service)\n",
    "    sparql.setQuery(query)\n",
    "    sparql.setReturnFormat(JSON)\n",
    "    result = sparql.query().convert()\n",
    "    return json_normalize(result['results']['bindings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = select('''\n",
    "SELECT ?sLabel ?population ?s WHERE {\n",
    "  ?s p:P31/ps:P31 wd:Q515 ;\n",
    "     wdt:P17 wd:Q37 ;\n",
    "     wdt:P1082 ?population .\n",
    "  SERVICE wikibase:label { bd:serviceParam wikibase:language \"lt\". }\n",
    "}\n",
    "ORDER BY DESC(?population)\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>population.datatype</th>\n",
       "      <td>http://www.w3.org/2001/XMLSchema#decimal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population.type</th>\n",
       "      <td>literal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population.value</th>\n",
       "      <td>532762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s.type</th>\n",
       "      <td>uri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s.value</th>\n",
       "      <td>http://www.wikidata.org/entity/Q216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sLabel.type</th>\n",
       "      <td>literal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sLabel.value</th>\n",
       "      <td>Vilnius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sLabel.xml:lang</th>\n",
       "      <td>lt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            0\n",
       "population.datatype  http://www.w3.org/2001/XMLSchema#decimal\n",
       "population.type                                       literal\n",
       "population.value                                       532762\n",
       "s.type                                                    uri\n",
       "s.value                   http://www.wikidata.org/entity/Q216\n",
       "sLabel.type                                           literal\n",
       "sLabel.value                                          Vilnius\n",
       "sLabel.xml:lang                                            lt"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['population'] = data['population.value'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f80eaa5c978>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ISUmhVatWnDx5kq5du96wz/LGJiJyN8vMzLR0CHeMq39R1j2zTsq/dVP+y8bHx+e65+6q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4cgCFCr1WjXrh0UCoWkjLe3N27fvm2ux8PDw6J+b29v8zHUarXFcUzHerJM/etJoVDAzc3NfCxq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\nbjbl6h8hEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80ea9c39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['sLabel.value', 'population']].set_index('sLabel.value').sort_values('population', ascending=True).plot.barh()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}